High throughput and rapid biological evaluation of small molecules is an essential factor in drug discovery and development. Direct-to-biology (D2B), whereby compound purification is foregone, has emerged as a viable technique in time efficient screening, specifically for PROTAC design and biological evaluation. However, one notable limitation is the prerequisite of high yielding reactions to ensure the desired compound is indeed the compound responsible for biological activity. Herein, we report a machine learning based yield-assay deconfounder capable of deconvoluting low yield from low potency to identify false negatives. We validated this approach by identifying promising SARS-CoV-2 main protease inhibitors with nanomolar activity that rivaled potency observed from the standard D2B workflow. Furthermore, we show how our framework can be utilized in a broad, in silico screen to produce compounds of similar potency as a D2B assay.

中文翻译：

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通过机器学习对直接生物学工作流程中弱效的低产量进行解卷积

小分子的高通量和快速生物学评价是药物发现和开发的重要因素。直接生物学 (D2B) 放弃了化合物纯化，已成为一种高效筛选的可行技术，特别是用于 PROTAC 设计和生物学评估。然而，一个显着的限制是高产率反应的先决条件，以确保所需的化合物确实是负责生物活性的化合物。在此，我们报告了一种基于机器学习的产量测定去混杂器，能够将低产量与低效力去卷积，以识别假阴性。我们通过鉴定具有纳摩尔级活性、可与标准 D2B 工作流程中观察到的效力相媲美的有前景的 SARS-CoV-2 主要蛋白酶抑制剂来验证这种方法。此外，我们展示了如何在广泛的计算机筛选中利用我们的框架来生产与 D2B 测定具有相似效力的化合物。